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Area of Science:

Computer Vision
Sensor Technology
Machine Learning

Background:

3D time-of-flight (ToF) sensors with single-photon avalanche diodes (SPADs) provide depth mapping for autonomous systems.
Limitations include low lateral resolution and signal-to-background ratio (SBR) under high ambient light, hindering scene interpretation.
Existing methods struggle with low-resolution depth data.

Purpose of the Study:

To develop a deep learning approach for enhancing 3D ToF depth data.
To address challenges of low resolution and noise in ToF imaging.
To enable real-time depth data processing for low-latency applications.

Main Methods:

A 3D convolutional neural network (CNN) was trained using synthetic depth sequences.
The CNN performs denoising and upscaling (×4) of depth data.
The approach was validated using both synthetic and real 3D ToF data.

Main Results:

The proposed CNN significantly improves depth map quality by denoising and upscaling.
Experimental results demonstrate effectiveness on both synthetic and real-world ToF data.
GPU acceleration allows processing at over 30 frames per second.

Conclusions:

The 3D CNN effectively enhances 3D ToF depth data, overcoming resolution and SBR limitations.
The real-time processing capability makes it suitable for critical applications like obstacle avoidance.
This method advances the usability of ToF sensors in demanding environments.